Plotly

Analyze Data & Create Plots

library(tidyverse)

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library(p8105.datasets)
library(plotly)

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data("rest_inspec")

rest_inspec = 
    rest_inspec %>%
    select(boro, cuisine_description, inspection_date, 
           critical_flag, score) %>%
    filter(critical_flag != "NA", boro != "Missing", score != "NA")

#Scatter plot of mean score by borough over time
rest_inspec %>%
  group_by(boro, inspection_date) %>%
  summarize(mean_score = mean(score)) %>%
  plot_ly(
    x = ~inspection_date, y = ~mean_score, type = "scatter", mode = "markers",
    color = ~boro, alpha = 0.5, colors = "viridis")  

## `summarise()` has grouped output by 'boro'. You can override using the
## `.groups` argument.

#Bar chart of critical flags by cuisine
rest_inspec %>%
  filter(critical_flag == "Critical") %>%
  count(cuisine_description) %>%
  mutate(critical_flag = fct_reorder(cuisine_description, n)) %>%
  plot_ly(x = ~cuisine_description, y = ~n, type = "bar", color = ~cuisine_description, colors = "viridis")

#Boxplots of scores by criticality
rest_inspec %>%
  plot_ly(x = ~critical_flag, y = ~score, type = "box", color = ~critical_flag, colors = "viridis")